Resources

25Feb2019

Challenges For Augmented Automation

It's no news that today most companies are exploring or early on in their journey of RPA powered with artificial intelligence for obvious reasons including scale, cutting costs, improving process efficiency and performance. However, the key challenges for augmented automation remain to be:

The good news is that there are many new methods that are showing promising results such as:

Combination models or Meta-learning for unsupervised learning that uses fast reinforced learning and hyperparameter optimization (Finn, 2017); Machine is capable to learn new task after exposing to large tasks or data. Hyperparameter values are set before the training of the model, including a number of filters, dropout rate, learning rate, cropped image size, and layer type and size (Miikkulainen, et al., 2017).

Imagination machines, imagination science addresses the problem of generating samples that are “novel”, meaning they come from a distribution different from the one used in training. Imagination science also addresses the problem of causal reasoning to uncover simple explanations for complex events and uses analogical reasoning to understand novel situations. (Mahadevan, 2018)

Transfer learning, being able to learn without an explicit reward score, e.g. solely through linguistic feedback, and a general interface with no need for manually re-programming when applied to another domain (Baroni, et al., 2017)

In my view with new methods of processing data like imagination machines and transfer learning will take RPA and other AI applications to a new level of innovation.